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A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality
Minsik Lim1, Doyeon An1, Nayeong Son2
1Department of IT Convergence Engineering, Graduate School, Gachon University, Seongnam-si, Gyeonggi‑do, South Korea.
JMIR Medical Informatics
|May 21, 2026
Summary
This study presents a practical method for assessing electronic health record (EHR) completeness for medical research. The approach reveals data quality insights, crucial for enhancing the reliability of clinical data and artificial intelligence applications.
Area of Science:
- Medical Informatics
- Clinical Data Science
- Health Data Quality
Background:
- Data quality is critical for medical research and AI development.
- Assessing real-world data quality presents challenges due to diverse environments and uses.
- Practical, interpretable data quality assessment methods are needed.
Purpose of the Study:
- To propose a practical approach for assessing the completeness of electronic health records (EHRs) for medical research.
- To integrate structural, rule-based, and descriptive analyses for meaningful data quality interpretation.
- To demonstrate a method for measuring data quality dimensions in real-world clinical data.
Main Methods:
- Evaluated the completeness of a large-scale EHR dataset (2005-2023) from Gachon University Gil Medical Center.
- Employed a three-part assessment: structural completeness, rule-based evaluation, and descriptive analysis of completeness and diversity.
- Utilized clinical data quality assessment tools on 1,798,153 patient records.
Main Results:
- Structural assessment revealed 12.8% data table unavailability, particularly for clinician free-text.
- Rule-based assessment found significant missingness in vocabulary fields (30.6%) and observation data (23.8%).
- Descriptive analysis showed balanced gender distribution (49.3% male, 50.7% female) and high Korean racial representation (96.7%).
Conclusions:
- Demonstrated a practical, multiperspective approach to measuring data quality dimensions in EHRs.
- Provided insights into EHR completeness, essential for reliable medical research.
- Findings support researchers in applying data quality assessments and dimensions in clinical studies.
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